Generalisation Power Analysis for finding a stable set of features using evolutionary algorithms for feature selection

作者：

Highlights：

• EC algorithms for feature selection may return different feature subsets whenever they are run

• Generalisation Power Analysis (GPA) evaluates feature subsets over multiple classifiers

• Experiments show that GPA outperforms alternative approaches for finding a stable set of features

• GPA can help EC algorithms find a stable set of features needed for constructing machine learning models

摘要

•EC algorithms for feature selection may return different feature subsets whenever they are run•Generalisation Power Analysis (GPA) evaluates feature subsets over multiple classifiers•Experiments show that GPA outperforms alternative approaches for finding a stable set of features•GPA can help EC algorithms find a stable set of features needed for constructing machine learning models

论文关键词：Feature selection,Generalisation Power Analysis,Generalisation Power Index,Machine learning,Evolutionary computation,Feature selection stability

论文评审过程：Received 7 September 2020, Revised 21 August 2021, Accepted 24 August 2021, Available online 27 August 2021, Version of Record 13 September 2021.

论文官网地址：https://doi.org/10.1016/j.knosys.2021.107450